Explainable planning is widely accepted as a pre-requisite for autonomous agents to
successfully work with humans. While there has been a lot of research on generating …

There is a growing interest within the AI research community in developing autonomous
systems capable of explaining their behavior to users. However, the problem of computing …

As increasingly complex AI systems are introduced into our daily lives, it becomes important
for such systems to be capable of explaining the rationale for their decisions and allowing …

Planning for real-world applications requires algorithms and tools with the ability to handle
the complexity such scenarios entail. However, meeting the needs of such applications …

Abstraction is an important technique utilized by humans in model building and problem
solving, in order to figure out key elements and relevant details of a world of interest. This …

Abstraction is a well-known approach to simplify a complex problem by over-approximating
it with a deliberate loss of information. It was not considered so far in Answer Set …

Symbolic representations have attracted significant attention in optimal planning. Binary
Decision Diagrams (BDDs) form the basis for symbolic search algorithms. Closely related …

We present a principled way of extending a classical AI planning formalism with systems of
state constraints, which relate-sometimes determine-the values of variables in each state …

Abstraction heuristics are a popular method to guide optimal search algorithms in classical
planning. Cost partitionings allow to sum heuristic estimates admissibly by partitioning …

Abstract Recent breakthroughs in Artificial Intelligence (AI) have brought the dream of
developing and deploying complex AI systems that can potentially transform everyday life …